As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

In recent years, considerable research has been dedicated to the application of neural
models in the field of natural language generation (NLG). The primary objective is to …

Existing dialog state tracking (DST) models are trained with dialog data in a random order,
neglecting rich structural information in a dataset. In this paper, we propose to use …

Task-oriented dialogue systems are either modularized with separate dialogue state
tracking (DST) and management steps or end-to-end trainable. In either case, the …

We study a family of data augmentation methods, substructure substitution (SUB2), for
natural language processing (NLP) tasks. SUB2 generates new examples by substituting …

Text augmentation is a strategy for increasing the diversity of training examples without
explicitly collecting new data. Owing to the efficiency and effectiveness of text augmentation …

This paper presents the autoencoder-generative adversarial network (AGAN) in the analysis
of mammograms. The AGAN architecture is used to augment the data by generating …

Advancements in conversational systems have revolutionized information access,
surpassing the limitations of single queries. However, developing dialogue systems requires …

Military target identification is one of the first tasks of modern counter-terrorism operations,
and military target detection methods based on unmanned system platforms can effectively …

Prior work has demonstrated that data augmentation is useful for improving dialogue state
tracking. However, there are many types of user utterances, while the prior method only …